Gender-Inclusive Fellowship Naming and Equity, Diversity, and Inclusion in Radiology: An Analysis of Radiology Department Websites in Canada and the United States

Purpose: Transgender and gender non-binary (TGNB) individuals face numerous inequalities in healthcare and there is substantial work to be done in fostering TGNB culturally competent care in radiology. A radiology department’s online presence and use of gender-inclusive language are essential in promoting an environment of equity, diversity, and inclusion (EDI). The naming of radiology fellowships and continuing medical education (CME) courses with terminology such as “Women’s Imaging” indicates a lack of inclusivity to TGNB patients and providers, which could result in suboptimal patient care. Methods: We conducted a cross-sectional analysis of all institutions in Canada and the United States (US) offering training in Breast Imaging, Women’s Imaging, or Breast and Body Imaging. Data was collected from each institution’s radiology department website pertaining to fellowship names, EDI involvement, and CME courses. Results: 8 Canadian and 71 US radiology fellowships were identified. 75% of Canadian and 90% of US fellowships had gender-inclusive names. One (12.5%) Canadian and 29 (41%) US institutions had EDI Committees mentioned on their websites. Among institutions publicly displaying CME courses about breast/body or women’s imaging, gender-inclusive names were used in only 1 (25%) of the Canadian CME courses, compared to 81% of the US institutions. Conclusions: Most institutions in Canada and the US have gender-inclusive names for their radiology fellowships pertaining to breast and body imaging. However, there is much opportunity to and arguably the responsibility for institutions in both countries to increase the impact and visibility of their EDI efforts through creation of department-specific committees and CME courses.

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

Anatomical Breast Imaging Derived Parameters Do Not Provide Incremental Information In Prediction of Nonvisualization of Sentinel Lymph Nodes On Lymphoscintigraphy

Background: Accurate sentinel lymph node (SLN) staging is essential for both prognosis and treatment in patients with breast cancer. However, the preoperative lymphoscintigraphy may fail to visualize the SLN. The aim of this retrospective study was to investigate whether parameters derived from anatomical breast imaging can predict SLN nonvisualization on lymphoscintigraphy. For this single-center retrospective study all data of mammography, breast magnetic resonance imaging (MRI), and lymphoscintigraphy of SLN procedures from January 2016 to April 2021 were collected and reviewed from the Amsterdam UMC electronic health records database.Results: A total of 758 breast cancer patients were included in this study. The SLN nonvisualization rate was 29.7% on lymphoscintigraphy. Multivariable analysis showed that age ≥ 70 years (P = 0.019; OR: 1.82; 95% CI: 1.10–3.01), BMI ≥ 30 kg/m2 (P = 0.031; OR: 1.59; 95% CI: 1.04–2.43), and nonpalpable tumors (P = 0.034; OR: 1.54; 95% CI: 1.03–2.04) were independent predictors of SLN nonvisualization. Differences in tumor size, Breast Imaging-Reporting and Data System (BI-RADS) classification, or breast density were not significantly associated with SLN nonvisualization.Conclusions: This study shows that, by using a multivariable analysis, risk factors for SLN nonvisualization in breast cancer patients during preoperative lymphoscintigraphy are age ≥70 years, BMI ≥30 kg/m2, and nonpalpable tumors. Parameters derived from mammography or breast MRI, however, are not useful to predict SLN nonvisualization on lymphoscintigraphy.